t x > 0 {\displaystyle t_{x}>0} 0}" loading="lazy"> such that for every t ∈ [ 0 , t x ] , {\displaystyle t\in [0,t_{x}],} a 0 + t x ∈ A . {\displaystyle a_{0}+tx\in A.} [1] Geometrically, this means A {\displaystyle A} is radial at a 0 {\displaystyle a_{0}} if for every x ∈ X , {\displaystyle x\in X,} there is some (non-degenerate) line segment (depend on x {\displaystyle x} ) emanating from a 0 {\displaystyle a_{0}} in the direction of x {\displaystyle x} that lies entirely in A . {\displaystyle A.} "> t x > 0 {\displaystyle t_{x}>0} 0}" loading="lazy"> such that for every t ∈ [ 0 , t x ] , {\displaystyle t\in [0,t_{x}],} a 0 + t x ∈ A . {\displaystyle a_{0}+tx\in A.} [1] Geometrically, this means A {\displaystyle A} is radial at a 0 {\displaystyle a_{0}} if for every x ∈ X , {\displaystyle x\in X,} there is some (non-degenerate) line segment (depend on x {\displaystyle x} ) emanating from a 0 {\displaystyle a_{0}} in the direction of x {\displaystyle x} that lies entirely in A . {\displaystyle A.} ">

Radial set

In mathematics, a subset of a linear space is radial at a given point if for every there exists a real such that for every [1] Geometrically, this means is radial at if for every there is some (non-degenerate) line segment (depend on ) emanating from in the direction of that lies entirely in

Every radial set is a star domain although not conversely.

Relation to the algebraic interior

The points at which a set is radial are called internal points.[2][3] The set of all points at which is radial is equal to the algebraic interior.[1][4]

Relation to absorbing sets

Every absorbing subset is radial at the origin and if the vector space is real then the converse also holds. That is, a subset of a real vector space is absorbing if and only if it is radial at the origin. Some authors use the term radial as a synonym for absorbing.[5]

See also

References

  1. ^ a b Jaschke, Stefan; Küchler, Uwe (2000). "Coherent Risk Measures, Valuation Bounds, and ()-Portfolio Optimization" (PDF). Humboldt University of Berlin.
  2. ^ Aliprantis & Border 2006, p. 199–200.
  3. ^ John Cook (May 21, 1988). "Separation of Convex Sets in Linear Topological Spaces" (PDF). Retrieved November 14, 2012.
  4. ^ Nikolaĭ Kapitonovich Nikolʹskiĭ (1992). Functional analysis I: linear functional analysis. Springer. ISBN 978-3-540-50584-6.
  5. ^ Schaefer & Wolff 1999, p. 11.